Object detection techniques generally involve searching an image for reference patterns. One such technique, for example described in publication titled “Partially Parallel Architecture for AdaBoost-Based Detection With Haar-like Features”, (Hiromoto et al., IEEE transactions on circuits and systems for video technology, VOL. 19, No. 1, January 2009) involves the use of Haar-like features. A search window is moved across an image, and at each location, blocks of pixels within the search window are analysed based on the Haar-like features using a cascade of tests in order to detect patterns that can indicate the presence of a certain object such as a face.
While the use of cascades of tests for object recognition tends to provide good rates of detection, it is relatively demanding on processing resources. In certain applications, it would be beneficial to be able to use face detection for auto-focus and automatic exposure controls. In particular, in electronic image capturing devices such as digital cameras or mobile telephones, prior to capturing and storing to memory a “final” image, the image data from the image sensor can be used to detect focus and exposure conditions, enabling the image sensor and/or a lens system to be controlled appropriately during the subsequent image capture. Given that when faces are present in an image it is particularly desirable that they have correctly adapted focus and exposure, knowing the location of any faces in the image would be beneficial.
However, due to the computationally demanding nature of object detection, there is a technical problem in performing such detection in real time or near real-time, particularly on compact devices having limited processing resources.